Blending of agricultural products via hyperspectral imaging and analysis

ABSTRACT

Provided is a method for blending of agricultural product utilizing hyperspectral imaging. At least one region along a sample of agricultural product is scanned using at least one light source of different wavelengths. Hyperspectral images are generated from the at least one region. A spectral fingerprint for the sample of agricultural product is formed from the hyperspectral images. A plurality of samples of agricultural product is blended based on the spectral fingerprints of the samples according to parameters determined by executing a blending algorithm.

FIELD

Disclosed herein is a method and system for the blending of agriculturalproducts, such as tobacco, using hyperspectral imaging and analysis.Embodiments disclosed herein can be practiced with other agriculturalproducts, including but not limited to, tea, grapes, coffee, vegetables,fruit, nuts, breads, cereals, meat, fish and other plant or animalparts.

ENVIRONMENT

Certain agricultural products, such as cultivated tobacco plants tea,spinach, etc., are harvested for their leaves, which may then be driedand cured as in the case of tobacco or tea. In the manufacture ofcigarettes and other tobacco products, blends of different types oftobaccos are frequently employed, with three main types of tobacco usedin U.S. blends. These tobacco types are Virginia or flue-cured, Burleyand Oriental. Similar blending of different varieties and qualities oftea leaves is also done. In the following, the method and the system areillustrated for application in the tobacco industry, though the samemethod and system could be used for other agricultural products usingleaves like tea and spinach, or in other packaged agricultural productsusing fruits, grapes, tomatoes, and other vegetables.

Regarding tobacco leaves, Virginia, or flue-cured tobacco, is alsocalled “bright tobacco” since it turns a bright yellow-orange colorduring curing. It grows particularly well in Georgia, Virginia and theCarolinas. Flue-curing is a heat driven process that reduces the risk ofmold and promotes chemical changes that improve the sensory quality ofthe tobacco.

Burley tobacco is a slightly lighter green leaf than Virginia tobacco.It requires heavier soils and is grown in Maryland, Kentucky andelsewhere. After harvesting, Burley tobaccos are air cured to reduce therisk of mold and improve sensory quality. In contrast to flue-curing,for Burley tobacco, air-curing takes place under ambient conditions.

Oriental tobacco is the smallest and hardiest of all tobacco types,grown principally in the Balkans, Turkey and the Middle East. Theseconditions and a high planting density create an aromatic flavor.Oriental tobacco is typically sun-cured.

Typically, tobacco materials are used in blended form. For example,certain popular tobacco blends, commonly referred to as “American”blends, comprise mixtures of flue-cured tobacco, burley tobacco andOriental tobacco. Such blends, in many cases, contain tobacco materialsthat have processed forms, such as processed tobacco stems (e.g.,cut-rolled stems, cut-rolled-expanded stems or cut-puffed stems), volumeexpanded tobacco (e.g., puffed tobacco, such as dry ice expanded tobacco(DIET), preferably in cut filler form). Tobacco materials also can havethe form of reconstituted tobaccos (e.g., reconstituted tobaccosmanufactured using paper-making type or cast sheet type processes).Tobacco reconstitution processes traditionally convert portions oftobacco that normally might otherwise be treated as waste intocommercially useful forms. For example, tobacco stems, recyclable piecesof tobacco and tobacco dust can be used to manufacture processedreconstituted tobaccos of fairly uniform consistency. See, for example,Tobacco Encyclopedia, Voges (Ed.) p. 44-45 (1984), Browne, The Design ofCigarettes, 3.sup.rd Ed., p. 43 (1990) and Tobacco Production, Chemistryand Technology, Davis et al. (Eds.) p. 346 (1999). Variousrepresentative tobacco types, processed types of tobaccos, types oftobacco blends, cigarette components and ingredients, and tobacco rodconfigurations, also are set forth in U.S. Pat. No. 4,836,224 to Lawsonet al.; U.S. Pat. No. 4,924,883 to Perfetti et al.; U.S. Pat. No.4,924,888 to Perfetti et al.; U.S. Pat. No. 5,056,537 to Brown et al.;U.S. Pat. No. 5,159,942 to Brinkley et al.; U.S. Pat. No. 5,220,930 toGentry; U.S. Pat. No. 5,360,023 to Blakley et al.; U.S. Pat. No.6,730,832 to Dominguez et al.; and U.S. Pat. No. 6,701,936 to Shafer etal.; U.S. Patent Application Publication Nos. 2003/0075193 to Li et al.;2003/0131859 to Li et al.; 2004/0084056 to Lawson et al.; 2004/0255965to Perfetti et al.; 2005/0066984 to Crooks et al.; and 2005/0066986 toNestor et al.; PCT WO 02/37990 to Bereman; and Bombick et al., Fund.Appl. Toxicol., 39, p. 11-17 (1997); which are incorporated herein byreference.

Dark air-cured tobacco is a type of tobacco used mainly for chewingtobacco, snuff, cigars, and pipe blends. Most of the world production ofsuch tobacco is confined to the tropics; however, sources of darkair-cured tobacco are also found in Kentucky, Tennessee, and Virginia.Dark air-cured tobacco plants are characterized by leaves with arelatively heavy body and such tobacco plants are typically highlyfertilized and topped low to around 10-12 leaves. See TobaccoProduction, Chemistry and Technology, Davis et al. (Eds.) pp. 440-451(1999).

These tobacco types may be further broken down into subgroups which maydepend upon where the tobacco is grown, which part of the plant it istaken from, weather conditions and other characteristics that relate tothe quality of the tobacco, including aroma, color, maturity, anduniformity. It should be understood that similar situation prevails inother leafy vegetables, fruits and other agricultural produce as well.

One tobacco plant can produce leaves of varying properties. The sensory,physical, chemical and visual properties of a tobacco leaf are, to alarge extent, but not wholly determined by the leaf position on theplant. For example, the leaves at the top of the plant have moreexposure to the sun than the ones at the bottom and typically containhigher levels of compounds that are sensorially important. In addition,the chemical content of the leaf can vary widely depending on the typeof tobacco, soil, environmental, prevailing weather conditions, where itwas grown, the way it is cured and how it is matured.

As in the case of any agricultural product, tobacco may be characterizedby a wide variety of physical, chemical, and/or biological properties,characteristics, features, and behavior, which are associated withvarious aspects relating to agriculture, agronomy, horticulture, botany,environment, geography, climate, and ecology of the tobacco crop andplants thereof from which tobacco leaves are derived. Moreover, theseproperties of tobacco leaves may be spatially (i.e., geographically) andtemporally (i.e., seasonally) variable or dependent. Such definition andcharacterization of tobacco leaves are directly translatable andextendable to defining and characterizing tobacco samples.

The forms disclosed herein are generally focused on the domainsencompassing blending of tobacco bales, and are specifically focused onthe domains encompassing automatic blending of tobacco that can beperformed via hyperspectral imaging and analysis. However, it will beappreciated by those skilled in the art that the technique could be usedfor applications involving other agricultural products, including, butnot limited to, tea, spinach, fruits, vegetables, etc.

In the general technique of hyperspectral imaging, one or more objectsin a scene or sample are affected in a way, such as excitation byincident electromagnetic radiation supplied by an external source ofelectromagnetic radiation upon the objects, which causes each object toreflect, scatter and/or emit electromagnetic radiation featuring aspectrum.

Hyperspectral imaging and analysis is a combined spectroscopy andimaging type of analytical method involving the sciences andtechnologies of spectroscopy and imaging. By definition, hyperspectralimaging and analysis is based on a combination of spectroscopy andimaging theories, which are exploitable for analyzing samples ofphysical, chemical, and/or biological matter in a highly unique,specialized, and sophisticated, manner.

Hyperspectral images generated by and collected from a sample of mattermay be processed and analyzed by using automatic pattern recognitionand/or classification type data and information processing and analysis,for identifying, characterizing, and/or classifying, the physical,chemical, and/or biological properties of the hyperspectrally imagedobjects in the sample of matter.

There remains a need for a method and system for automatic qualityevaluation and blending agricultural products, such as tobacco, viahyperspectral imaging and analysis.

SUMMARY

Disclosed herein is a method and system for quality evaluation andblending of agricultural products, including tobacco, via hyperspectralimaging and analysis. The method and system disclosed herein providehigh sensitivity, high resolution, and high speed during operation, in asimple yet highly efficient, cost effective and commercially applicablemanner.

In one aspect, disclosed herein is a method for blending of agriculturalproduct utilizing hyperspectral imaging. At least one region along asample of agricultural product is scanned using at least one lightsource of appropriate spectral wavelengths. Hyperspectral images aregenerated from the at least one region. A spectral fingerprint for thesample of agricultural product is formed from the hyperspectral images.A plurality of samples of agricultural product is blended based on thespectral fingerprints of the samples according to parameters determinedby a computer, based upon an intelligent algorithm devised for theapplication.

In another aspect, provided is a method for determining blendingparameters for an agricultural product utilizing hyperspectral imaging.Multiple regions along a sample of a desirable agricultural product arescanned using at least one light source of appropriate spectralwavelengths. Hyperspectral images are generated from the multipleregions. A spectral fingerprint is formed for the sample from thehyperspectral images. One or more features of the spectral fingerprintare correlated to desirable sensory attributes of the sample.

Certain forms disclosed herein are focused on the domain encompassingthe testing of agricultural products, including tobacco bales, lots orsamples, based on measuring, analyzing, and determining micro scaleproperties, characteristics, features, and parameters, generally withrespect to individual bales, lots or samples, and specifically withrespect to individual leaves contained within the bales, lots orsamples. A wide variety of physical, chemical, and/or biologicalproperties may be determined. Blending of agricultural products,including tobacco, is provided. Certain forms disclosed herein may beperformed in an automatic on-line manner, via hyperspectral imaging andanalysis.

Certain forms disclosed herein include definition and use ofhyperspectrally detectable and classifiable codes. In turn, theclassified agricultural product such as tobacco contained within thatparticular tobacco bale, lot or sample is usable as part of a procedurefor blending that particular tobacco bale, lot or sample.

Certain forms disclosed herein provide for tracking and accounting forthe spatial (i.e., geographical) and temporal (i.e., seasonal)variability of physical, chemical, and/or biological properties andbehavior of agricultural product such as tobacco leaves. Such spatialand temporal variability or dependency of agricultural product such astobacco leaves can be uniquely tracked and accounted for by use of thehyperspectrally detectable and classifiable codes and can therefore beincorporated into a procedure for blending tobacco.

Certain forms disclosed herein are implemented according to a temporalgated type of hyperspectral imaging and analysis. Accordingly, in someforms thereof, a method and system for the blending of agriculturalproduct such as tobacco via temporal gated hyperspectral imaging andanalysis is provided.

In a still yet further aspect, provided is a method of creating adatabase for blending an agricultural product. The method utilizeshyperspectral imaging and includes the steps of (a) obtaining a darkimage and a reference image for calibration; (b) analyzing the referenceimage to obtain calibration coefficients; (c) obtaining a hyperspectralimage for an agricultural sample; (d) removing dark values andnormalizing the agricultural sample image; (e) applying calibrationcoefficients to compensate for fluctuations in system operatingconditions; (f) repeating steps (c)-(e) for all agricultural samples;and (g) storing all hyperspectral sample hypercubes to form thedatabase.

In one form, the computer database is stored in a computer readablemedium.

In still yet another aspect, provided is a method for classifying acandidate agricultural product variety for use as a substitute for ablend component of a blended agricultural product, the method comprisingthe steps of: (a) establishing a spectral fingerprint for theagricultural product blend component to be substituted; (b) establishinga spectral fingerprint for the candidate agricultural product variety;(c) comparing the spectral fingerprint for the candidate agriculturalproduct variety to the spectral fingerprint for the agricultural productblend component; and (d) if the spectral fingerprint for the candidateagricultural product variety is substantially similar to the spectralfingerprint for the agricultural product blend component, substitute thecandidate agricultural product variety for at least a portion of theblend component in the blended agricultural product.

Certain forms disclosed herein are implemented by performing steps orprocedures, and sub-steps or sub-procedures, in a manner selected fromthe group consisting of manually, semi-automatically, fullyautomatically, and combinations thereof, involving use and operation ofsystem units, system sub-units, devices, assemblies, sub-assemblies,mechanisms, structures, components, and elements, and, peripheralequipment, utilities, accessories, and materials. Moreover, according toactual steps or procedures, sub-steps or sub-procedures, system units,system sub-units, devices, assemblies, sub-assemblies, mechanisms,structures, components, and elements, and, peripheral equipment,utilities, accessories, and materials, used for implementing aparticular form, the steps or procedures, and sub-steps orsub-procedures are performed by using hardware, software, and/or anintegrated combination thereof, and the system units, sub-units,devices, assemblies, sub-assemblies, mechanisms, structures, components,and elements, and peripheral equipment, utilities, accessories, andmaterials, operate by using hardware, software, and/or an integratedcombination thereof.

For example, software used, via an operating system, for implementingcertain forms disclosed herein can include operatively interfaced,integrated, connected, and/or functioning written and/or printed data,in the form of software programs, software routines, softwaresubroutines, software symbolic languages, software code, softwareinstructions or protocols, software algorithms, or a combinationthereof. For example, hardware used for implementing certain formsdisclosed herein can include operatively interfaced, integrated,connected, and/or functioning electrical, electronic and/orelectromechanical system units, sub-units, devices, assemblies,sub-assemblies, mechanisms, structures, components, and elements, and,peripheral equipment, utilities, accessories, and materials, which mayinclude one or more computer chips, integrated circuits, electroniccircuits, electronic sub-circuits, hard-wired electrical circuits, or acombination thereof, involving digital and/or analog operations. Certainforms disclosed herein can be implemented by using an integratedcombination of the just described exemplary software and hardware.

In certain forms disclosed herein, steps or procedures, and sub-steps orsub-procedures can be performed by a data processor, such as a computingplatform, for executing a plurality of instructions. Optionally, thedata processor includes volatile memory for storing instructions and/ordata, and/or includes non-volatile storage, for example, a magnetichard-disk and/or removable media, for storing instructions and/or data.Optionally, certain forms disclosed herein include a network connection.Optionally, certain forms disclosed herein include a display device anda user input device, such as a touch screen device, keyboard and/ormouse.

BRIEF DESCRIPTION OF THE DRAWINGS

The forms disclosed herein are illustrated by way of example, and not byway of limitation, for the case of blending tobacco while manufacturingtobacco products in the figures of the accompanying drawings and inwhich like reference numerals refer to similar elements and in which:

FIG. 1 presents, in block diagram form, a first stage of an automaticblending system, in accordance herewith;

FIG. 2 presents, in block diagram form, a second stage of an automaticblending system, in accordance herewith;

FIG. 3 presents an example of an implementation of a system for scanningan agricultural product employing hyperspectral imaging and analysis, inaccordance herewith;

FIG. 4 presents a method for analyzing data to create a spectrallibrary, in accordance herewith;

FIG. 5 presents a method for analyzing data to create a spectrallibrary, in accordance herewith;

FIG. 6 presents a process for determining the composition of a productbased on a database of spectral fingerprints for the samples, inaccordance herewith;

FIG. 7 presents a plot showing the classification of different tobaccotypes and grades based on hyperspectral signatures;

FIG. 8 presents a plot showing the classification of various tobaccostalk positions based on hyperspectral signatures; and

FIG. 9 presents a plot comparing the classification of twelve differentcandidate tobacco varieties to three tobacco blend components usinghyperspectral signatures.

DETAILED DESCRIPTION

Various aspects will now be described with reference to specific formsselected for purposes of illustration. It will be appreciated by thoseskilled in the art that the spirit and scope of the apparatus, systemand methods disclosed herein are not limited to the selected forms.Moreover, it is to be noted that the figures provided herein are notdrawn to any particular proportion or scale, and that many variationscan be made to the illustrated forms. Reference is now made to FIGS.1-8, wherein like numerals are used to designate like elementsthroughout.

Each of the following terms written in singular grammatical form: “a,”“an,” and “the,” as used herein, may also refer to, and encompass, aplurality of the stated entity or object, unless otherwise specificallydefined or stated herein, or, unless the context clearly dictatesotherwise. For example, the phrases “a device,” “an assembly,” “amechanism,” “a component,” and “an element,” as used herein, may alsorefer to, and encompass, a plurality of devices, a plurality ofassemblies, a plurality of mechanisms, a plurality of components, and aplurality of elements, respectively.

Each of the following terms: “includes,” “including,” “has,” “having,”“comprises,” and “comprising,” and, their linguistic or grammaticalvariants, derivatives, and/or conjugates, as used herein, means“including, but not limited to.”

It is to be understood that the various forms disclosed herein are notlimited in their application to the details of the order or sequence,and number, of steps or procedures, and sub-steps or sub-procedures, ofoperation or implementation of forms of the method or to the details oftype, composition, construction, arrangement, order and number of thesystem, system sub-units, devices, assemblies, sub-assemblies,mechanisms, structures, components, elements, and configurations, and,peripheral equipment, utilities, accessories, and materials of forms ofthe system, set forth in the following illustrative description,accompanying drawings, and examples, unless otherwise specificallystated herein. The apparatus, systems and methods disclosed herein canbe practiced or implemented according to various other alternative formsand in various other alternative ways, as can be appreciated by thoseskilled in the art.

It is also to be understood that all technical and scientific words,terms, and/or phrases, used herein throughout the present disclosurehave either the identical or similar meaning as commonly understood byone of ordinary skill in the art, unless otherwise specifically definedor stated herein. Phraseology, terminology, and, notation, employedherein throughout the present disclosure are for the purpose ofdescription and should not be regarded as limiting.

Moreover, all technical and scientific words, terms, and/or phrases,introduced, defined, described, and/or exemplified, in the abovesections, are equally or similarly applicable in the illustrativedescription, examples and appended claims.

Steps or procedures, sub-steps or sub-procedures, and, equipment andmaterials, system units, system sub-units, devices, assemblies,sub-assemblies, mechanisms, structures, components, elements, andconfigurations, and, peripheral equipment, utilities, accessories, andmaterials, as well as operation and implementation, of exemplary forms,alternative forms, specific configurations, and, additional and optionalaspects, characteristics, or features, thereof, of the methods, and ofthe systems, disclosed herein, are better understood with reference tothe following illustrative description and accompanying drawings.Throughout the following illustrative description and accompanyingdrawings, same reference notation and terminology (i.e., numbers,letters, and/or symbols), refer to same system units, system sub-units,devices, assemblies, sub-assemblies, mechanisms, structures, components,elements, and configurations, and, peripheral equipment, utilities,accessories, and materials, components, elements, and/or parameters.

As a means of illustration, the system will be described for applicationduring tobacco processing and product development, but substantially thesame system could be applied during the processing and productdevelopment of other agricultural products. Tobacco is packaged in theform of tobacco bales, graded and purchased from growers. A tobacco baleis a large, substantially rectangular shaped package of tobacco leavesand stems, tightly bound with a strong cord or wire or loosely packed inboxes. A typical Burley or Virginia tobacco bale may have dimensions onthe order of at least about 1.2 meters per side and a correspondingvolume on the order of about 1.7 cubic meters.

In a general manner, testing of tobacco samples may be divided into twomajor aspects: a macro scale aspect, and a micro scale aspect, wherethese two major aspects relate to different types of properties,characteristics, features, and parameters of tobacco.

The first major aspect is associated with macro scale properties oftobacco generally with respect to whole individual tobacco bales orlots, but not specifically with respect to single or individual tobaccoleaves contained within the tobacco bales or lots. Primary examples ofmacro scale testing include weight of individual tobacco bales; physicalsize dimensions of individual tobacco bales; packing density ofindividual tobacco bales; void volume of individual tobacco bales; andmoisture (water) content or humidity of individual tobacco bales orlots.

The second major aspect is associated with micro scale properties oftobacco, also generally with respect to individual tobacco bales orlots, but specifically with respect to individual tobacco leavescontained within the tobacco bales or lots, and more specifically withrespect to a wide variety of numerous possible physical, chemical,and/or biological properties, characteristics, features, and parameters,of single or individual tobacco leaves contained within a given tobaccobale or lot.

Primary examples of such micro scale testing include physical shape andsize dimensions of individual tobacco leaves; coloring of individualtobacco leaves; moisture (water) content of individual tobacco leaves;types, distribution, and composition of organic and inorganic chemicalspecies or components of single or individual tobacco leaves; types,distribution, and composition of possible unknown or foreign matter orspecies on and/or within individual tobacco leaves; the behavior(activity and/or reactivity) of single or individual tobacco leaves inresponse to physical stimuli or effects, such as exposure toelectromagnetic radiation; activity and/or reactivity of single orindividual tobacco leaves in response to chemical stimuli or effects(such as exposure to aqueous liquids or to nonaqueous liquids; andactivity and/or reactivity of individual tobacco leaves in response tobiological stimuli or effects, such as exposure to biological organisms.

Ordinarily, it may be expected that the two major aspects or componentsof tobacco bale, lot or sample testing are essentially independent orseparate from each other. However, depending upon (1) the particularmacro scale and micro scale properties, characteristics, features, andparameters of the tobacco, and, depending upon (2) the particularproperties, characteristics, features, and behavior of individualtobacco leaves contained within the tobacco bales, lots or samples, anddepending upon (3) the particular context of tobacco bale, lot or sampletesting, then, one major aspect may influence another major aspect.

The forms disclosed herein are generally focused on the domainsencompassing blending of tobacco lots, blend components or samples, andare specifically focused on the domains encompassing automatic blendingof tobacco lots or samples that can be performed via hyperspectralimaging and analysis. However, it should be understood that the formsdisclosed herein could be applied to other domains encompassing blendingof e.g. tea, fruits during the production of fruit juices, grapes forthe production of wines as well as a vast array of other agriculturalproducts.

As may be appreciated, the systems and methods described herein havemultiple utilities. With regard to tobacco processing, in one form,blend components from different lots of tobacco (i.e., different harvestyears, different locations, different weather conditions, etc.) may beblended to achieve desired attributes for that variety's use as ablending component. In another form, different blend components (i.e.,different varieties) may be blended to produce a final blended tobacco,such as for a cut filler to be used to produce a particular brand ofcigarette, cigar or smokeless product. For purposes of example, and notof limitation, a particular brand of cigarette could generally comprisetwice the amount of Burley tobacco to flue cured, but be adjusted toprovide the same sensory experience using the information revealed bythe systems and methods described herein.

In hyperspectral imaging, a field of view of a sample is scanned andimaged while the sample is exposed to electromagnetic radiation. Duringthe hyperspectral scanning and imaging there is generated and collectedrelatively large numbers of multiple spectral images, one-at-a-time,but, in an extremely fast sequential manner of the objects emittingelectromagnetic radiation at a plurality of wavelengths and frequencies,where the wavelengths and frequencies are associated with differentselected portions or bands of an entire hyperspectrum emitted by theobjects. A hyperspectral imaging and analysis system can be operated inan extremely rapid manner for providing exceptionally highly resolvedspectral and spatial data and information of an imaged sample of matter,with high accuracy and high precision, which are fundamentallyunattainable by using standard spectral imaging and analysis.

In general, when electromagnetic radiation in the form of light, such asthat used during hyperspectral imaging, is incident upon an object, theelectromagnetic radiation is affected by one or more of the physical,chemical, and/or biological species or components making up the object,by any combination of electromagnetic radiation absorption, diffusion,reflection, diffraction, scattering, and/or transmission, mechanisms.Moreover, an object whose composition includes organic chemical speciesor components, ordinarily exhibits some degree of fluorescent and/orphosphorescent properties, when illuminated by some type ofelectromagnetic radiation or light, such as ultra-violet (UV), visible(VIS), or infrared (IR), types of light. The affected electromagneticradiation, in the form of diffused, reflected, diffracted, scattered,and/or transmitted, electromagnetic radiation emitted by the object isdirectly and uniquely related to the physical, chemical, and/orbiological properties of the object, in general, and of the chemicalspecies or components making up the object, in particular, and thereforerepresents a unique spectral fingerprint or signature pattern type ofidentification and characterization of the object.

A typical spectral imaging system consists of an automated measurementsystem and analysis software. The automated measurement system includesoptics, mechanics, electronics, and peripheral hardware and software,for irradiating, typically using an illuminating source, a scene orsample, followed by measuring and collecting light emitted, for example,by fluorescence, from objects in the scene or sample, and for applyingcalibration techniques best suited for extracting desired results fromthe measurements. Analysis software includes software and mathematicalalgorithms for analyzing, displaying, and presenting, useful resultsabout the objects in the scene or sample in a meaningful way.

The hyperspectral image of a scene or a sample could be obtained fromcommercially available hyperspectral imaging cameras from Surface OpticsCorporation of San Diego, Calif., among others, or custom builtaccording to the user needs.

Hyperspectral imaging can be thought of as a combination of spectroscopyand imaging. In spectroscopy a spectra is collected at a single point.Spectra contain information about the chemical composition and materialproperties of a sample, and consist of a continuum of values thatcorrespond to measurements at different wavelengths of light. Incontrast, traditional cameras collect data of thousands of points. Eachpoint or pixel contains one value (black and white image) or threevalues for a color image, corresponding to colors, red, green, and blue.Hyperspectral cameras combine the spectral resolution of spectroscopyand the spatial resolution of cameras. They create images with thousandsof pixels that contain an array of values corresponding to lightmeasurements at different wavelengths. Or, in other words, the data ateach pixel is a spectrum. Together the pixels and the correspondingspectra create a multi-dimensional image cube. The amount of informationcontained in an image cube is immense, and provides a very detaileddescription of the underlying sample.

Each spectral image is a three dimensional data set of voxels (volume ofpixels) in which two dimensions are spatial coordinates or position, (x,y), in an object and the third dimension is the wavelength, (λ), of theemitted light of the object, such that coordinates of each voxel in aspectral image may be represented as (x, y, λ). Any particularwavelength, (λ), of imaged light of the object is associated with a setof spectral images each featuring spectral fingerprints of the object intwo dimensions, for example, along the x and y directions, wherebyvoxels having that value of wavelength constitute the pixels of amonochromatic image of the object at that wavelength. Each spectralimage, featuring a range of wavelengths of imaged light of the object isanalyzed to produce a two dimensional map of one or more physicochemicalproperties, for example, geometrical shape, form, or configuration, anddimensions, and/or chemical composition, of the object and/or ofcomponents of the object, in a scene or sample.

Spectral profiles treat image cubes as a collection of spectra andprovide a detailed and comprehensive description of the image cube as awhole. They use a set of characteristic spectra and their relativeoccurrences within an image cube to summarize the material compositionof the sample. The number of characteristic spectra extracted willdepend upon the variability in the material, and normally range from afew to a few dozen. Spectral profiles are created by matching eachspectra in an image cube to a characteristic spectra. The number ofspectra matched to each characteristic spectra are counted andnormalized to create the spectral profile of an image cube. Thus,spectral profiles can be thought of as a fingerprint derived from thehyperspectral image cube of the tobacco sample.

In hyperspectral imaging, multiple images of each object are generatedfrom object emitted electromagnetic radiation having wavelengths andfrequencies associated with different selected parts or bands of anentire spectrum emitted by the object. For example, hyperspectral imagesof an object are generated from object emitted electromagnetic radiationhaving wavelengths and frequencies associated with one or more of thefollowing bands of an entire spectrum emitted by the object: the visibleband, spanning the wavelength range of about 400-700 nanometers, theinfra-red band, spanning the wavelength range of about 700-3000nanometers, and the deep infra-red band, spanning the wavelength rangeof about 3-12 microns. If proper wavelengths and wavelength ranges areused during hyperspectral imaging, data and information of thehyperspectral images could be optimally used for detecting and analyzingby identifying, discriminating, classifying, and quantifying, the imagedobjects and/or materials, for example, by analyzing different signaturespectra present in pixels of the hyperspectral images.

A high speed hyperspectral imaging system is often required fordifferent types of repeatable and non-repeatable chemical and physicalprocesses taking place during the sub-100 millisecond time scale, whichcannot, therefore, be studied using regular hyperspectral imagingtechniques. Combustion reactions, impulse spectro-electrochemicalexperiments, and inelastic polymer deformations, are examples of suchprocesses. Remote sensing of objects in distant scenes from rapidlymoving platforms, for example, satellites and airplanes, is anotherexample of a quickly changing observable that is often impossible torepeat, and therefore requires high speed hyperspectral imaging.

Disclosed herein, is a method, and system, for the blending ofagricultural products, such as tobacco, via hyperspectral imaging andanalysis. In certain forms thereof, provided are methodologies,protocols, procedures and equipment that are highly accurate and highlyprecise, in that they are reproducible and robust, when evaluatingagricultural products, such as tobacco. The testing methodologiesdisclosed herein exhibit high sensitivity, high resolution, and highspeed during automatic on-line operation.

Certain forms disclosed herein are specifically focused on the domainencompassing measuring, analyzing, and determining, micro scaleproperties, characteristics, features, and parameters of agriculturalproducts, such as tobacco, generally with respect to individual tobaccobales, lots or samples, and specifically with respect to single orindividual tobacco leaves contained within the tobacco bales, lots orsamples, and more specifically with respect to a wide variety ofnumerous possible physical, chemical, and/or biological properties,characteristics, features, and parameters of single or individualtobacco leaves contained within a given tobacco bale, lot or sample. Inone form, provided is an automatic on-line blending system employinghyperspectral imaging and analysis.

Certain forms disclosed herein use what will be referred to as“hyperspectrally detectable and classifiable codes.” As used herein, a“hyperspectrally detectable and classifiable code” is a micro scaleproperty, characteristic, feature, or parameter of a particular bulkagricultural product, such as a tobacco bale, lot or sample, which ishyperspectrally detectable by hyperspectral imaging and analysis in amanner that the resulting hyperspectral data and information, forexample, hyperspectral “fingerprint” or “signature” patterns are usablefor classifying at least part of a single or individual tobacco leafcontained within that particular tobacco bale, lot or sample. In turn,the classified part of the single or individual tobacco leaf containedwithin that particular tobacco bale, lot or sample is usable as part ofa procedure for blending that particular tobacco bale, lot or sample.

Accordingly, a “hyperspectrally detectable and classifiable code” isdefined, generally with respect to a particular individual agriculturalproduct, such as a tobacco bale, lot or sample, and specifically withrespect to a single or individual tobacco leaf contained within theparticular tobacco bale, and more specifically with respect to aphysical, chemical, and/or biological property, characteristic, feature,or parameter, of that single or individual tobacco leaf contained withinthat particular tobacco bale, lot or sample. The hyperspectrallydetectable and classifiable codes are usable as part of a procedure for(uniquely and unambiguously) blending tobacco bales, lots or samples.

Primary examples of micro scale testing for generating hyperspectrallydetectable and classifiable codes, include: physical(geometrical/morphological) shape or form and size dimensions of singleor individual tobacco leaves; coloring of single or individual tobaccoleaves; moisture (water) content of, or within, single or individualtobacco leaves; type, distribution, and compositional make-up, of(organic and inorganic) chemical species or components, of single orindividual tobacco leaves; types, distribution, and compositionalmake-up, of possible unknown or foreign (physical, chemical, and/orbiological) matter or species and aspects thereof on, and/or within,single or individual tobacco leaves; activity and/or reactivity ofsingle or individual tobacco leaves in response to physical stimuli oreffects, such as exposure to electromagnetic radiation; activity and/orreactivity of single or individual tobacco leaves in response tochemical stimuli or effects, such as exposure to aqueous liquids or tonon-aqueous (organic based) liquids; and activity and/or reactivity ofsingle or individual tobacco leaves in response to biological stimuli oreffects, such as exposure to biological organisms; physical(geometrical/morphological) shape or form and size dimensions of singleor individual tobacco leaves; coloring of single or individual tobaccoleaves; moisture content of, or within, single or individual tobaccoleaves; types, distribution, and compositional make-up, of (organic andinorganic) chemical species or components, of single or individualtobacco leaves; types, distribution and compositional make-up ofpossible unknown or foreign (physical, chemical, and/or biological)matter or species and aspects thereof on, and/or within, single orindividual tobacco leaves; activity and/or reactivity of single orindividual tobacco leaves in response to physical stimuli or effects,such as exposure to electromagnetic radiation; activity and/orreactivity of single or individual tobacco leaves in response tochemical stimuli or effects (such as exposure to aqueous (water based)liquids or to non-aqueous (organic based) liquids); and activity and/orreactivity of single or individual tobacco leaves in response tobiological stimuli or effects, such as exposure to biological organisms.

In one form, tracking and accounting for the spatial (i.e.,geographical) and temporal (i.e., seasonal) variability or dependency ofphysical, chemical, and/or biological properties, characteristics,features, and behavior, of agricultural products, such as tobacco bales,lots or samples and tobacco leaves are provided. Such spatial andtemporal variability or dependency of tobacco leaves, and therefore, oftobacco bales, lots or samples, can be uniquely and unambiguouslytracked and accounted for by use of the hyperspectrally detectable andclassifiable codes and can therefore be incorporated into a procedurefor (uniquely and unambiguously) blending tobacco bales, lots orsamples. It should be understood that the technique could be used forproducts other than tobacco as well, like tea, fruits while making fruitproducts, grapes during wine manufacturing, other agricultural products,and the like.

Quality and character of tobacco or other agricultural products such astobacco vary due to factors such as, for example, particular plantvariety, growth region, weather conditions, fertilizer and cropprotection agents used, etc. To make a high quality, consistent product,producers blend various agricultural product samples, which also allowsfor design of a sensorially acceptable product. Sensory attributes of ablend depends upon the components in the blend. The sensory attributesare usually determined by subjective evaluation using product panels.Blending is done by expert blenders who rely on subjective judgmentabout the raw material available, to develop the blend. Often, it isnecessary for the blenders to make a product from the individual rawmaterial, for example, in the case of tobacco, to make cigarettes andother tobacco products from individual tobacco grades, and then usesubjective sensory evaluation to determine the blend. Thus, blending canbe a time consuming and laborious process, with final result dependentupon the experience and skill of the blenders and subjective evaluationfrom the sensory panels.

As such, it may be appreciated that a large number of variables impacttobacco blending, with the set of blend criteria including, but notlimited to, year of harvest, type of tobacco, leaf location on thetobacco plant, grade, growing region, seasonal weather conditions, etc.,as those skilled in the art would plainly recognize.

In the following, the invention is described in detail for the case ofblending tobacco. However, it should be understood that tobacco is usedonly to illustrate the methods and systems contemplated herein and notas limiting the application of the methods and systems described herein.Referring to FIGS. 1 and 2, disclosed herein is a blending system 100 todetermine the sensory attributes 102, which can be used to formconsistent, quality blends 104 based upon available raw materials. Thesystem 100 uses spectral fingerprints 106 and 124 obtained byhyperspectral imaging system 110. Each spectral fingerprint 106 or 124gives a measure of the physical and chemical characteristics of thetobacco sample 108 or 122 (or other agricultural raw material). Thephysical and chemical characteristics determine the sensory attributesof the different tobacco samples 108 or 122. By building a database ofthe spectral fingerprints 106 of different types and grades of tobacco108 and using blending schemes 112 employed by human blenders, anintelligent system 118 can be built based upon statistical predictionand/or neural network and artificial intelligence techniques 120 todevelop an automatic blending system 100. The algorithm can be optimizedfor cost reduction by including the cost of the different batches oftobacco samples as one of the variables used in the optimization scheme.

Referring to FIG. 1, in a first stage 101 of the system 100, a databaseis built with the existing tobacco samples 108 and the hyperspectralfingerprints 106 and the subjective sensory attributes 116 for differentlots of tobacco samples. An intelligent system 118 is built based upon aneural network or an artificial intelligence algorithm 120 that providesa mapping of the hyperspectral signature with the subjective sensoryattributes 116 and evaluation score from expert blenders 120. The costsof individual tobacco lots can also be used as an independent parameterin the algorithm to optimize the blending scheme for sensory attributesand cost effectiveness. At the end of the first stage 101, there isformed a composite hyperspectral signature for one or more acceptableblends, which can be correlated to satisfactory sensory attributes.

Referring to FIG. 2, in a second phase 103, spectral fingerprints 124 oftobacco bales, lots or samples 122 are obtained. The cost of each ofthese tobacco bales, lots or samples 122 are also obtained and used asan input to the system 100. Intelligent system 114 will determine, usingthe expert system 118 developed in the first phase 101, the bestblending parameters for minimum cost and acceptable sensory attributesusing the input parameters, the spectral fingerprints 124, and the costand availability of tobacco bales, lots or samples.

Accordingly, provided is a method for blending of agricultural productutilizing hyperspectral imaging. At least one region along a sample ofagricultural product is scanned using at least one light source of asingle or different wavelengths. Hyperspectral images are generated fromthe at least one region. A spectral fingerprint for the sample ofagricultural product is formed from the hyperspectral images. Aplurality of samples of agricultural product is blended based on thespectral fingerprints of the samples according to parameters determinedby a computer processor executing a blending algorithm.

The method may comprise scanning multiple regions along the sample ofagricultural product using at least one light source of a single ordifferent wavelengths; and generating hyperspectral images from themultiple regions. The method may further comprise determining a code forthe sample.

The blending may form a blended agricultural product with desirablesensory attributes. The method may further comprise determining one ormore features of a spectral fingerprint that corresponds to thedesirable sensory attributes. Determining one or more features of aspectral fingerprint that correspond to the desirable sensory attributesmay comprise the steps of: (i) scanning at least one region along asample of a desirable agricultural product using at least one lightsource of different wavelengths; (ii) generating hyperspectral imagesfrom the at least one region; and (iii) forming a spectral fingerprintfor the sample of the desirable agricultural product from thehyperspectral images. The desirable agricultural product may be ablended agricultural product blended by a human. The method may furthercomprise correlating one or more features of the spectral fingerprintfor the sample of the desirable agricultural product to the desirablesensory attributes.

The agricultural product may comprise tobacco. The tobacco may be in theform of a bale, lot or sample. At least one light source may bepositioned to minimize the angle of incidence of each beam of light withthe sample being imaged.

Cost of the samples may be a factor used by the computer processor inthe blending step.

The method may further comprise repeating the steps of scanning at leastone region along a sample of agricultural product using at least onelight source of different wavelengths, generating hyperspectral imagesfrom the at least one region, and forming a spectral fingerprint for thesample of agricultural product from the hyperspectral images, for aplurality of samples of agricultural product prior to blending theplurality of samples of agricultural product. The method may furthercomprise, prior to the blending step, storing data about the spectralfingerprints of the plurality of samples of agricultural product withina computer storage means, and storing at least a portion of at leastsome of the plurality of samples of agricultural product; the blendingstep may comprise blending at least a portion of at least some of theplurality of samples of agricultural product stored in the previousstep.

Further provided is a system for blending of agricultural product,according to the methods described above.

Also provided is a method for determining blending parameters for anagricultural product utilizing hyperspectral imaging. Multiple regionsalong a sample of a desirable agricultural product are scanned using atleast one light source of different wavelengths. Hyperspectral imagesare generated from the multiple regions. A spectral fingerprint isformed for the sample from the hyperspectral images. One or morefeatures of the spectral fingerprint are correlated to desirable sensoryattributes of the sample.

The method may further comprise storing data about the spectralfingerprint within a computer storage means, and repeating the steps ofscanning multiple regions along a sample of a desirable agriculturalproduct using at least one light source of a single or differentwavelengths, generating hyperspectral images from the multiple regions,forming a spectral fingerprint for the sample from the hyperspectralimages, storing data about the spectral fingerprint within a computerstorage means, using a plurality of desirable agricultural products. Thedesirable agricultural product may be a blended agricultural productblended by a human.

Additionally provided is a system for determining blending parametersfor an agricultural product, according to the methods described above.

Referring now to FIG. 3, a system 10 for grading an agricultural productP employing hyperspectral imaging and analysis is shown in schematicform. The system 10 includes at least one light source 12 for providinga beam of light. As shown, the at least one light source 12 may bemounted on an arm 14 for positioning at least one light source 12 inproximity to the agricultural product (not shown), which may bepositioned on platform 50. In one form, arm 14 is mounted to frame 16 ofcabinet 20 and may be either fixed thereto or moveably positionable, aswill be described hereinbelow. As shown in FIG. 3, a second light source18 may also be provided and mounted to frame 16 of cabinet 20 or,optionally, to an arm (not shown), which in turn is mounted to frame 16of cabinet 20.

In one form, the at least one light source 12 for providing a beam oflight of different wavelengths comprises a tungsten, halogen or a xenonlight source. In another form, the at least one light source 12 forproviding a beam of light comprises a mercury light source. In yetanother form, the at least one light source or the second light source18 comprises an ultraviolet light source for use in providing a chemicalsignature of the agricultural product P. This optional ultraviolet lightsource adds an additional media of classification that provides a betterunderstanding of an agricultural product's quality. In still yet anotherform, the at least one light source 12 comprises a xenon light source,the second light source 18 comprises a mercury light source and a thirdlight source (not shown) comprises an ultraviolet light source.

In one form, the at least one light source 12 and/or the second lightsource 18 may be positioned to minimize the angle of incidence of a beamof light with the agricultural product P.

In order to segregate ambient light from the light provided by system10, walls (not shown) may be added to cabinet 20 to form an enclosure toprovide a dark-room-like environment for system 10.

The hyperspectral image of a scene or a sample is obtained usinghyperspectral imaging camera 24.

Referring still to FIG. 3, a computer 40 having a processor capable ofrapidly handling system data is provided and programmed to compare thedetected component wavelengths to a database of previously analyzedagricultural products to identify agricultural product P. Computer 40may be a personal computer having an Intel® Core™2 Quad or otherprocessor. Computer 40 may also control the operation of the system 10and the positioning of the head unit 20 about agricultural product P. Adevice 34 for providing an uninterrupted source of power to computer 40may be provided, such devices readily available from a variety ofcommercial sources. As is conventional, computer 40 may also include akeyboard 36 and monitor 38 to enable input and system monitoring by auser U. A regulated power supply 34 may be provided to assure that atightly controlled source of power is supplied to system 10.

Test results are based on the scanning and counting of individualsamples, each comprising dozens of scans, and each sample classifiedusing spectral band features, spectral finger prints (SFP), majorspectral representative components, purity and quality of each majorcompound (component, SFP), relative quantity of each SFP and,optionally, crystallization and morphological features.

A plurality of samples of agricultural products, such as tobacco bales,lots or samples are scanned. As indicated, for agricultural productssuch as tobacco, a significant number of samples should be scanned inorder that the impact of sample or lot variability is reduced. Inpractice, it has been observed that the impact due to this variabilitycan be reduced when the number of samples N is about 5 to about 25.However, by carefully selecting representative samples, fewer samplescould be used to incorporate all the normal variations observed in aparticular product lot. Applying this technique to tobacco, tobaccosamples may be scanned using xenon and/or mercury and/or tungsten,and/or halogen light sources and an optional ultraviolet light sourcemay be used for chemical signature classification.

In operation, the light source(s) is activated (one, two, three or morespot lights in parallel to the region of interest (ROI)), permittingredundant data to be gathered. A plurality of regions of interest (suchas by way of example, but not of limitation, a 20 cm×20 cm area for eachROI) is scanned for each sample to provide one, two, three or morehyperspectral images. Scanning is performed and the reflection spectralgrading signature and optional fluorescence spectral chemical signaturereceived. The images are then saved and a database (including labelsidentifying the particular sample and/or lot) is thus formed from thecombined information obtained for the N samples

During scanning, the hyperspectral camera system provides a threedimensional hyperspectral image cube. The image cube, which may be, byway of example, but not of limitation, on the order of about a 696 pixelby 520 pixel array. Such a picture or frame would thus contain about361,920 pixels. As may be appreciated by those skilled in the art, eachpixel may contain about 128, 256, 500 or more spectra points atdifferent wavelengths for an agricultural product such as tobacco.

Referring now to FIG. 4, an algorithm 200 for use in the system andmethods described herein will now be disclosed. In step 210, a darkimage and reference image are obtained for use in system calibration. Instep 220, the reference image is analyzed and calibration coefficientsare obtained. In step 230, a hyperspectral image of a tobacco sample isobtained. In step 240, using the information obtained duringcalibration, dark values are removed and the sample image normalized. Instep 250, calibration coefficients are applied to compensate forfluctuations in operating conditions (e.g., light intensity, ambientconditions, etc.). In step 260, steps 230-250 are repeated for allsamples and the data so obtained is added in step 270 to the database ofspectral hypercubes (whole dataset). It should be understood that thealgorithm for creating the database as illustrated in FIG. 4 isreferenced to tobacco samples by way of illustration only and not aslimiting. The same steps could be used to create the whole spectraldatabase for application in other agricultural products like tea, fruitsgrapes or other products. The result is 350, a spectral library for allthe samples that could be used to form the final product which containsthe spectral fingerprints of the dataset found in step 340, and theunique spectra found in step 330.

As such, disclosed herein is a method of creating a database forcontrolling a manufacturing process for producing an agriculturalproduct. The method utilizes hyperspectral imaging and includes thesteps of (a) obtaining a dark image and a reference image forcalibration; (b) analyzing the reference image to obtain calibrationcoefficients; (c) obtaining a hyperspectral image for an agriculturalsample; (d) removing dark values and normalizing the agricultural sampleimage; (e) applying calibration coefficients to compensate forfluctuations in system operating conditions; (f) repeating steps (c)-(e)for all agricultural samples; and (g) storing all hyperspectral samplehypercubes to form the database.

In one form, the computer database is stored in a computer readablemedium.

Referring now to FIG. 5, a method for analyzing data to create aspectral library of samples 300, in accordance herewith is shown.Spectral library 300 is formed by obtaining a dataset in step 310. Instep 320, the dataset is preprocessed. Unique spectra, indicative of thedataset, are identified in step 330. In step 340, spectral distributions(spectral fingerprints) are found for each of the samples using theunique spectra found in step 330.

In some forms, following imaging, several data processing routines areperformed to reduce noise, increase consistency, enhance spectra forfeature extraction, and reduce computation time. The data processingroutines are summarized below.

Spectral binning is used to produce more consistent signals, reduce filesize, and decrease processing time. Spectral binning is an operationperformed on each spectra of the image cube, and consists of summationof adjacent wavelengths to produce a down-sampled spectra. Down-sampledspectra have less resolution, but increased signal to noise ratio. Asampling rate was chosen that creates a signal with maximum compressionand minimal loss of fidelity. This is followed by median filtering toreduce the noise and increase the signal to noise ratio.

Spatial Binning is applied to the image cube, and consists ofdown-sampling via summation of adjacent pixels. Pixels contain spectra,so spatial binning results in summation of adjacent spectra. Thisincreases signal to noise ratio, reduces file size, and decreasesprocessing time. Information loss is minimal because the camera isselected to provide a high resolution. Summation allows for each pixel'sspectra to contribute to the down-sampled spectra, and adjacent pixelsare usually from the same part of the leaf, which tend to have similarspectra. Similar effects may be achieved by using a Gaussian filter toincrease spatial coherency and to reduce “salt and pepper” pixelclassification. A Gaussian filter may be useful when spatial reductionis not desirable.

In some forms, image correction may be performed by first collectingdark images twice daily. These are then used during processing toestimate and remove sensor noise. The dark image processing procedureconsists of two steps. Dark images' spectra, ^(→)d_(j)∈D_(i) are firstbinned to be consistent with other images cubes. Then the spectral mean,^(→)d^(mean;i) is calculated (Eq. 1) and used to estimate sensor noise.

$\begin{matrix}{{\overset{arrow}{S}}_{i}^{mean} = {{\sum\limits_{\;}^{\forall{{\overset{arrow}{s}}_{j} \in {S_{1}}_{\;}}}\;{\overset{arrow}{S}}_{j}}}} & (1)\end{matrix}$

During data processing, d^(mean);i is removed from each spectra of eachsample by subtraction. This is a standard method of removing sensornoise.

Reference image cubes, R_(i) are collected twice-daily and containspectra that can be used to measure and correct lightinginconsistencies. Applying a correction based on the reference image willalso eliminate any sensor drift or variation over time. This step isvery important as any changes in either the lighting conditions or thesensor response drift will adversely affect the system performance.Also, pixels of shadow have low signal to noise ratio and should beeliminated.

Most signals tend to have a maximum peak created by the spectralsignature of the light whose value is proportional to the amount oflight hitting the sensor, and the sensor's response. For this reasonshadow detection is applied using maximum peak thresholding.

${pixel} = \{ \begin{matrix}{{not}{\mspace{11mu}\;}{shadow}\text{:}} & {{\max( {\overset{arrow}{s}}_{i} )} \geq {thresh}} \\{{shadow}\text{:}} & {{\max( {\overset{arrow}{s}}_{i} )} < {thresh}}\end{matrix} $where max(^(→)si) returns the maximum component of the spectra, ^(→)si.

Spectra with a max peak less than a user-defined threshold are tagged asshadow spectra, and ignored during spatial binning, spectral binning,local spectra extraction, and spectra matching.

Image cubes of tobacco samples contain thousands of spectra, many ofwhich are nearly identical and correspond to similar material propertiesand sensorial effects. The information contained in the image cube canbe summarized as a spectral profile using a set of characteristicspectra and their occurrence rate within a sample. Construction of aspectral profile consists of two primary steps.

-   1. Spectra Extraction—finding characteristic spectra.-   2. Spectra Matching—matching an image cube's spectra to    characteristic spectra.

The first step of building a spectral profile is to create a set ofcharacteristic spectra often called end-members. End-members are oftenmanually selected by choosing pixels that are known to correspond to aspecific class or contain a unique material. For many agriculturalproducts, including tobacco, distinct characteristics can be verydifficult to detect manually and class specific spectra arenon-existent. In such cases an automated spectra extraction technique ispreferable.

Spectra extraction differs from other automated end-member extractiontechniques in that it divides the spectral feature space using an evenlyspaced grid. Spectra extraction finds all spectra in a data set that aremore dissimilar than a user-defined threshold, α*. The assumption isthat if two spectra are more similar than α* they represent identicalmaterials and can be considered duplicates. By finding all dissimilarspectra in a data set, all unique materials can be found. Otherautomated spectra extraction algorithms are often associated withun-mixing models, which assume individual pixels contain a combinationof unique spectral signatures from multiple materials. SequentialMaximum Angle Convex Cone (SMACC) and Support Vector Machine-BasedEnd-member extraction are two examples. The technique disclosed hereinwas chosen for simplicity. Un-mixing models may not be appropriate forsmaller scale agricultural imaging, where individual pixel size is inmillimeters as opposed to aerial imaging, where individual pixel size isusually in meters. The spectra extraction procedure disclosed hereinfirst analyzes image cubes independently in a step called local spectraextraction. The results are then combined during global spectraextraction. Extraction in this order decreases processing time andallows for outliers of individual image cubes to be eliminated.

Once the characteristic spectra of the data set have been extracted,spectra matching step is applied on each image cube. Each^(→)s_(j)∈S_(j) is matched with the most similar ^(→)c_(k)∈C_(all). Asan image cube is analyzed, a tally of the number of matches for each^(→)c_(k)∈C_(all) is kept as a spectral profile, ^(→)p_(i). Eachcomponent of ^(→)p_(i) corresponds to the number of matches for a single^(→)c_(k)∈C_(all). Once all ^(→)s_(j)∈S_(i) have been matched ^(→)p_(i)is normalized and represents the percent occurrence rate of each^(→)c_(k)∈C_(all) in Si.

The inclusion of unidentified pixels can be advantageous for certainscenarios such as when tobacco samples are contaminated with non-tobaccomaterial, if shadow detection is unreliable, or if only a selected fewspectra should be included in the spectral profiles. Rather than forcinga match with the most similar ^(→)c_(k)∈C_(all), Spectra that are moredissimilar than α** to all ^(→)c_(k)∈C_(all) are counted asunidentified. Unidentified pixels do not contribute to the spectralprofile. α** is a user-defined parameter, and larger values of α** willallow more dissimilar spectra to match to ^(→)c_(k)∈C_(all), while smallvalues will allow matches with only similar spectra. Setting a high α*will force a match with the closest ^(→)c_(k)␣C_(all).

Since previous hyperspectral image classification problems have focusedon pixel by pixel classification, spectra matching is often the goal ofhyperspectral image analysis. These applications often make use ofmachine learning algorithms such as support vector machines and decisiontrees to classify spectra. Matching in this manner requires a trainingset of characteristic spectra which is not practical. Fully automatedtechniques are preferable, and also may use spectral feature fitting(SFF). SFF is designed to distinguish between spectra using specificfeatures of a spectra. While SFF can achieve successful results, SAM isa more appropriate measure, since it is intended to find the similaritybetween two spectra using all the bands as confirmed by the results.

The goal of feature selection is to choose a subset of features capableof summarizing the data with little or no information loss. It isapplied before classification to avoid dimensionality, which oftenreduces classification performance. Spectral profiles can containredundant features, particularly when the spectra extraction threshold,α* is low. Experimental work suggests that selection of an appropriateα*combined with the ability of support vector machines (SVM) to handleredundant and/or uninformative features eliminates the need for afeature selection step. However in some cases we found that choosing theoptimal subset of features using the Jeffreys-Matusita Distance as aninformation measure was effective.

In some forms, classification may be conducted using well-knownprocedures, by way of example and not of limitation, discriminantanalysis, SVM, neural networks, etc., as those skilled in the art willrecognize.

As may be appreciated, the amount of raw data is very large, due to thelarge number of pixels existing within a particular image cube. Aseparate spectral is obtained for each pixel. Advantageously, the methoddisclosed herein identifies a set of characteristic spectra for thewhole dataset. The composition of the spectra provides a signature forthe sample, with similar samples having similar signatures orfingerprints. Unique spectral fingerprints are then identified for eachblend grade.

Referring now to FIG. 6, a process for using the spectral library ordatabase of the samples developed in FIG. 5 to select the samples todevelop a particular product blend is illustrated. Database 400 is thedatabase or spectral library of blending grades developed as illustratedin FIG. 5. In step 410, a product blend is obtained based on eitherexpert evaluation or chemical/sensory attributes. In step 420, aspectral distribution is obtained for the particular product blend ofstep 410 as disclosed in the foregoing by taking a hyperspectral imageand processing the resulting image cube. This spectral distributionprovides a signature for the product blend. A product blend selectionalgorithm is used in step 430, which will provide the necessarycomposition of the available samples in the sample database forobtaining the desired product blend 410. Step 440 shows the output ofthe operation, i.e. the composition of the samples in the database tomake up the final ingredients for the particular product which willprovide the necessary sensory attributes for the product.

Advantageously, the method and system disclosed herein provides achemical imaging platform that enables speed and exceptionally highsensitivity, thus accurate and non-destructive nature, whereinmeasurement time is greatly reduced. This enables the tracking of themonitored material with high repeatability.

The method and system disclosed herein provides a highly sensitive hyperspectral imaging analyzer with co-sharing database capabilities. Thedigitized highly sensitive imaging system disclosed herein enables theimaging of materials components, more detailed observation and providesmore specific, sensitive, accurate and repeatable measurements. This maybe achieved using advanced image recognition technologies, as well asadaptive and collaborative data bases and statistical and optimizationalgorithms.

The method and system disclosed herein is capable of characterizing thecomposition of inorganic, organic, and chemical particulate mattersuspended in agricultural products such as tobacco. The instrument scansleaf or other samples, analyses the scanned samples' wavelengthsignature, performing trends analysis and compares the gathered data todata bases that may, in one form, be continuously updated. The systemthen generates reports for system users. A remote network may beprovided to support the gathering of data for integrated databaseconstruction as well as to support remote professional personnel in realtime.

In another aspect, the system and methods disclosed herein may beutilized to evaluate agricultural materials, including tobacco, for useas substitute blending materials in blended products, such ascigarettes, cigars, smokeless tobacco, tea, or the like. Potentialsubstitute blending materials are scanned and imaged using the systemand methods disclosed herein, then compared to samples previouslyevaluated and stored in a database, in order to predict the sensorialcharacteristics of the potential substitute material. If acceptable, thesystem can be utilized to determine the amount of the potentialsubstitute material that may be utilized in a finished blend. Onceagain, the cost of the potential substitute blending material may beutilized to optimize the cost and sensorial attributes of a finishedblend. Substitute blending materials may include genetically modifiedand/or hybridized agricultural materials, as well as conventionalmaterials.

Advantageously, the proposed method requires no sample preparation. Inoperation, linear calibration plots in the ppm range are obtained formono-component contamination and for simple compound mixtures in thismatrix. Non-contact, non-destructive, near real time on-line, automatedphysicochemical imaging, classification and analysis of a sample oftobacco leaves or other agricultural products is provided without theneed for consumable materials for sample processing. The system operatesusing algorithms and software packages, together with unique ultra-highresolution optical components and a two-dimensional sample positioningof regions of interest for generating, for example, five dimensional(5D) spectral images of the tobacco sample under analysis.

All or a portion of the devices and subsystems of the exemplary formscan be conveniently implemented using one or more general purposecomputer systems, microprocessors, digital signal processors,microcontrollers, and the like, programmed according to the teachings ofthe exemplary forms disclosed herein, as will be appreciated by thoseskilled in the computer and software arts.

In view thereof, in one form there is provided a computer programproduct for facilitating the blending of agricultural products,determining one or more features of a spectral fingerprint thatcorrespond to desirable sensory attributes, and/or determining blendingparameters, including one or more computer readable instructionsembedded on a tangible computer readable medium and configured to causeone or more computer processors to perform the steps described above andtransmitting information relating to the steps described above over acommunications link.

Appropriate software can be readily prepared by programmers of ordinaryskill based on the teachings of the exemplary forms, as will beappreciated by those skilled in the software art. Further, the devicesand subsystems of the exemplary forms can be implemented on the WorldWide Web. In addition, the devices and subsystems of the exemplary formscan be implemented by the preparation of application-specific integratedcircuits or by interconnecting an appropriate network of conventionalcomponent circuits, as will be appreciated by those skilled in theelectrical art(s). Thus, the exemplary forms are not limited to anyspecific combination of hardware circuitry and/or software.

Stored on any one or on a combination of computer readable media, theexemplary forms disclosed herein can include software for controllingthe devices and subsystems of the exemplary forms, for driving thedevices and subsystems of the exemplary forms, for enabling the devicesand subsystems of the exemplary forms to interact with a human user, andthe like. Such software can include, but is not limited to, devicedrivers, firmware, operating systems, development tools, applicationssoftware, and the like. Such computer readable media further can includethe computer program product of a form disclosed herein for performingall or a portion (if processing is distributed) of the processingperformed in implementing the methods disclosed herein. Computer codedevices of the exemplary forms disclosed herein can include any suitableinterpretable or executable code mechanism, including but not limited toscripts, interpretable programs, dynamic link libraries (DLLs), Javaclasses and applets, complete executable programs, Common Object RequestBroker Architecture (CORBA) objects, and the like. Moreover, parts ofthe processing of the exemplary forms disclosed herein can bedistributed for better performance, reliability, cost, and the like.

As stated above, the devices and subsystems of the exemplary forms caninclude computer readable medium or memories for holding instructionsprogrammed according to the forms disclosed herein and for holding datastructures, tables, records, and/or other data described herein.Computer readable medium can include any suitable medium thatparticipates in providing instructions to a processor for execution.Such a medium can take many forms, including but not limited to,non-volatile media, volatile media, transmission media, and the like.Non-volatile media can include, for example, optical or magnetic disks,magneto-optical disks, and the like. Volatile media can include dynamicmemories, and the like. Transmission media can include coaxial cables,copper wire, fiber optics, and the like. Transmission media also cantake the form of acoustic, optical, electromagnetic waves, and the like,such as those generated during radio frequency (RF) communications,infrared (IR) data communications, and the like. Common forms ofcomputer-readable media can include, for example, a floppy disk, aflexible disk, hard disk, magnetic tape, any other suitable magneticmedium, a CD-ROM, CDRW, DVD, any other suitable optical medium, punchcards, paper tape, optical mark sheets, any other suitable physicalmedium with patterns of holes or other optically recognizable indicia, aRAM, a PROM, an EPROM, a FLASH-EPROM, any other suitable memory chip orcartridge, a carrier wave or any other suitable medium from which acomputer can read.

The forms disclosed herein, as illustratively described and exemplifiedhereinabove, have several beneficial and advantageous aspects,characteristics, and features. The forms disclosed herein successfullyaddress and overcome shortcomings and limitations, and widen the scope,of currently known teachings with respect to the blending ofagricultural products such as tobacco bales, lots or samples.

The forms disclosed herein, provide for blending methodologies(protocols, procedures, equipment) for, by way of example, but not oflimitation, tobacco bales, lots or samples, which are highly accurateand highly precise (reproducible, robust), during tobacco bale finalselection processes. The forms disclosed herein, provide highsensitivity, high resolution, and high speed (fast, at short timescales), during automatic operation, in an optimum and highly efficient(cost effective) commercially applicable manner.

As may be appreciated, the performance of the methods and systemsdisclosed herein is dependent of the number of regions and samplesscanned, image sizes, light sources, filters, light source energystability, etc.

EXAMPLES Example 1

The operation of the system and a method of forming a database will nowbe described by way of this prophetic example.

The system is initiated and the light sources brought up to operatingtemperature. A dark image and reference image are obtained for systemcalibration. The reference image is analyzed and calibrationcoefficients are obtained. A hyperspectral image of a tobacco or otheragricultural sample is obtained.

Using the information obtained during the aforementioned calibration,dark values are removed and the sample image normalized. Calibrationcoefficients are applied to compensate for fluctuations in operatingconditions (e.g., light intensity, ambient conditions, etc.).Hyperspectral images for additional tobacco samples are obtained foradditional samples of interest and all data so obtained is added to thedatabase of spectral hypercubes (whole dataset).

A spectral library is formed from the database of spectral hypercubes byfirst preprocessing the data. Unique spectra, indicative of the datasetare identified and samples mapped to the spectral fingerprints soobtained. The unique spectra are then added to a spectral database ofblending grades.

Referring now to FIG. 7, a plot showing the classification of differenttobacco types and grades based on hyperspectral signatures is presented.As shown, unique spectral fingerprints were observed for the burley,flue cured and oriental grades tested, demonstrating the resolution andaccuracy of the system and methods disclosed herein.

Referring now to FIG. 8, a plot showing the classification of varioustobacco stalk positions based on hyperspectral signatures is presented.As shown, unique spectral fingerprints were observed for the variousstalk positions, demonstrating the resolution and accuracy of the systemand methods disclosed herein.

Example 2

A method for using the database created, in accordance herewith, willnow be described by way of this prophetic example.

When a new product is to be developed, or the components for matchingthe sensory character of an existing product is to be determined, first,a spectral distribution is obtained for this product based on theteachings of this invention. As described herein, a selection algorithmis used, the results of which provide the composition of the materialsnecessary to obtain the product.

Upon implementation in accordance with the foregoing teachings, thesystem will lessen or obviate the need to use sensorial panels in thedevelopment of new blends and in the quality control of blends usedcommercially.

Example 3

The following is provided as an illustrative example in accordance withthe above teachings.

In this example, a hypothetical blend of cut filler of a particularbrand of a commercial smoking article comprises components A, B and C.It is also contemplated that for each of those components, there are atleast three variations or “Lots” (such as three different stalkpositions or the like). Accordingly, first component A includes lots A′,A″ and A′″. Likewise for subcomponents B and C. The three components A,B, and C as well as the three variations or lots are provided as a meansof illustration only and should not be construed as limiting. Dependingupon the product blend and the material used there could be a pluralityof components and variations from a few to several dozen.

Referring to FIG. 1, the first step under the example is to establish alibrary of the “preferred blended attribute” or “P” of a particularblend for brand. In this example the library is constructed by a sensorypanel smoking test cigarettes according to a particular blend (i.e., A,B and C) and determining whether the blend meets the “preferred blendedattribute”. If so, the particular tested blend is hyper-spectral imagedas taught with reference to FIG. 1 to produce a hyper-spectral imagethat is known to provide the “preferred blended attribute” for theparticular brand.

That hyper-spectral image is then subjected to the step of finding its“unique spectra” by resolving the minimum number of spectra componentsnecessary to identify that which was imaged, using information theoreticapproaches such as maximum likelihood and/or principal componentextraction and other techniques known in the art. Once the “uniquespectra” are identified, the parameters of “P” are established and theassociated data is stored in an electronic library for future reference.The result might appear like so:P=(0.30−0.32)x+(0.50−0.55)y+(2.0−2.2)z

wherein x, y, z . . . are each members or “spectral elements” of the setcomprising the unique spectra and the digits are pixel values (or arange of pixel values) of the respective spectral element. The range ofthe coefficients specified (like 0.30 to 0.32 for the spectral feature xas given in the foregoing) is obtained by imaging multiple examples ofthe preferred product blend.

Further to the illustrative example, each lot of the blend subcomponentsA, B and C are characterized according to the same member set of “uniquespectra”. For example, for component A, the “unique spectra” of lots ofA may appear like so:A′=0.20x+0.40y+0.40zA″=0.22x+0.38y+0.45zA′″=0.18x+0.39y+0.37zEach lot of the other blend subcomponents B and C are characterized inthe same way:B′=0.70x+0.30y+0.00zB″=0.72x+0.33y+0.05zB′″=0.78x+0.39y+0.03zandC′=0.10x+0.60y+0.030zC″=0.12x+0.65y+0.35zC′″=0.18x+0.62y+0.29z

With the above characterization of P, together with similarcharacterization of each of the components A, B and C, it is resolvablethrough optimization techniques and/or iterative solution techniques andother analyses known in the art to resolve what combination (orproportions) of A (A′, A″, A′″), B (B′, B″, B′″)′ and C (C′, C″, C′″)will yield the values established for “P”.

Accordingly, as feedstocks of A, B, and C change, those changes can beaddressed mathematically to arrive at the same “preferred blendedattribute” or P, with lesser reliance on smoking panels and educatedguess-work on how to address the changes in feedstock.

Example 4

The following is provided as an illustrative example in accordance withthe above teachings.

The spectral library formed in accordance with Example 1 is employed forthis example. The library of spectral fingerprints for burley, fluecured and oriental grades is selected to identify substitute varietieshaving the sensorial characteristics of those grades. Twelve candidatevarieties, including natural and hybrid varieties are selected for thisexample.

The system is initiated and the light sources brought up to operatingtemperature. A dark image and reference image are obtained for systemcalibration. The reference image is analyzed and calibrationcoefficients are obtained.

Hyperspectral images for the twelve candidate varieties are obtained andall data so obtained is added to the database of spectral hypercubes(whole dataset). Using the information obtained during theaforementioned calibration, dark values are removed and the sampleimages normalized. Calibration coefficients are applied to compensatefor fluctuations in operating conditions (e.g., light intensity, ambientconditions, etc.).

A scatter plot is produced for the burley, flue cured and orientalvarieties of a blended agricultural product represented in the database.If desiring to replace one of those components with a more availableand/or cheaper variety, a spectral fingerprint is established for eachof the candidate varieties and plotted on the scatter plot, as shown inFIG. 9. As shown, candidate varieties 1, 5 through 8, and 10 through 12each fall within the range of oriental grade fingerprints and would beexpected to exhibit the sensorial characteristics of oriental gradetobacco, while candidate varieties 2 through 4, and 9 would not.Accordingly, candidate varieties 1, 5 through 8, and 10 through 12 mightindividually, or in combination, be used as a substitute for theoriginal, oriental component of the blend with minimal impact on thesensorial contribution of the replaced oriental blend component.

As such, in another aspect, provided is a method for classifying acandidate agricultural product variety for use as a substitute for ablend component of a blended agricultural product, the method comprisingthe steps of: (a) establishing a spectral fingerprint for theagricultural product blend component to be substituted; (b) establishinga spectral fingerprint for the candidate agricultural product variety;(c) comparing the spectral fingerprint for the candidate agriculturalproduct variety to the spectral fingerprint for the agricultural productblend component; and (d) if the spectral fingerprint for the candidateagricultural product variety is substantially similar to the spectralfingerprint for the agricultural product blend component, substitute thecandidate agricultural product variety for at least a portion of theblend component in the blended agricultural product.

As used herein the terms “adapted” and “configured” mean that theelement, component, or other subject matter is designed and/or intendedto perform a given function. Thus, the use of the terms “adapted” and“configured” should not be construed to mean that a given element,component, or other subject matter is simply “capable of” performing agiven function but that the element, component, and/or other subjectmatter is specifically selected, created, implemented, utilized,programmed, and/or designed for the purpose of performing the function.It is also within the scope of the present disclosure that elements,components, and/or other recited subject matter that is recited as beingadapted to perform a particular function may additionally oralternatively be described as being configured to perform that function,and vice versa.

Illustrative, non-exclusive examples of systems and methods according tothe present disclosure are presented in the following enumeratedparagraphs. It is within the scope of the present disclosure that anindividual step of a method recited herein, including in the followingenumerated paragraphs, may additionally or alternatively be referred toas a “step for” performing the recited action.

A1. A method for blending of agricultural product, the method utilizinghyperspectral imaging and comprising: (a) scanning at least one regionalong a sample of agricultural product using at least one light sourceof a single or different wavelengths; (b) generating hyperspectralimages from the at least one region; (c) determining a spectralfingerprint for the sample of agricultural product from thehyperspectral images; and (d) blending a plurality of samples ofagricultural product based on the spectral fingerprints of the samplesaccording to parameters determined by a blending algorithm.

A2. The method of paragraph A1, comprising: scanning multiple regionsalong the sample of agricultural product using at least one light sourceof a single or different wavelengths; and generating hyperspectralimages from the multiple regions.

A3. The method of paragraph A1, further comprising determining aphysicochemical code for the sample.

4. The method of claim 1, wherein the blending forms a blendedagricultural product with desirable sensory attributes.

A5. The method of paragraph A4, further comprising determining one ormore features of a spectral fingerprint that correspond to the desirablesensory attributes.

A6. The method of paragraph A5, wherein the blended agricultural productwith desirable sensory attributes is blended by a computer controlledsystem.

A7. The method of paragraph A5, wherein the blended agricultural productwith desirable sensory attributes is blended manually.

A8. The method of paragraph A5, further comprising: correlating one ormore features of the spectral fingerprint for the sample of thedesirable agricultural product to the desirable sensory attributes.

A9. The method of paragraph A1, wherein the agricultural productcomprises tobacco.

A10. The method of paragraph A9, wherein the tobacco is in the form of abale.

A11. The method of paragraph A10, wherein the at least one light sourceis positioned to minimize the angle of incidence of each beam of lightwith the bale of tobacco.

A12. The method of paragraph A1, wherein cost of the samples is a factorused by the computer processor in step (d).

A13. The method of paragraph A1, wherein the at least one light sourcefor providing a beam of light comprises a light source selected from thegroup consisting of a tungsten light source, a halogen light source, axenon light source, a mercury light source, an ultraviolet light source,and combinations thereof.

A14. The method of paragraph A1, further comprising repeating steps (a),(b), and (c) for a plurality of samples of agricultural product prior toblending the plurality of samples of agricultural product.

A15. The method of paragraph A14, further comprising, prior to step (d):storing data about the spectral fingerprints of the plurality of samplesof agricultural product within a computer storage means; and storing atleast a portion of at least some of the plurality of samples ofagricultural product; wherein step (d) comprises blending at least aportion of at least some of the plurality of samples of agriculturalproduct stored in the step of storing samples of agricultural product.

A16. A system for blending of agricultural product, according to themethod of paragraph A1.

B1. A method for determining blending parameters for an agriculturalproduct, the method utilizing hyperspectral imaging and comprising: (a)scanning multiple regions along a sample of a desirable agriculturalproduct using at least one light source of different wavelengths; (b)generating hyperspectral images from the multiple regions; (c) forming aspectral fingerprint for the sample from the hyperspectral images; and(d) correlating one or more features of the spectral fingerprint todesirable sensory attributes of the sample.

B2. The method of paragraph B1, further comprising: (e) storing dataabout the spectral fingerprint within a computer storage means; and (f)repeating steps (a), (b), (c), and (e) using a plurality of desirableagricultural products.

B3. The method of paragraph B1, wherein the desirable agriculturalproduct is a blended agricultural product blended manually.

B4. A system for determining blending parameters for an agriculturalproduct, according to the method of paragraph B1.

C1. A method of determining blending proportions amongst a plurality ofblend components for a product, the method comprising: resolving whethera sample blend meets a preferred blended attribute for the product andif so, applying hyperspectral imaging analysis and theoretic analysis toestablish a relationship P comprising unique spectra of the sample, saidunique spectra comprising at least two spectral elements x and y andvalues thereof; for individual blend components to be blended to producesaid preferred blended attribute for the product, establishing throughhyperspectral imaging analysis a characterization of the blend componentaccording to said spectral elements (at least x and y) of said uniquespectra P; and mathematically resolving from said characterizations whatproportions of the characterized blend components achieve the values ofsaid spectral elements of P.

D1. A method of creating a database for controlling a manufacturingprocess for producing an agricultural product, the method utilizinghyperspectral imaging and comprising: (a) obtaining a dark image and areference image for calibration; (b) analyzing the reference image toobtain calibration coefficients; (c) obtaining a hyperspectral image foran agricultural sample; (d) removing dark values and normalizing theagricultural sample image; (e) applying calibration coefficients tocompensate for fluctuations in system operating conditions; (f)repeating steps (c)-(e) for all agricultural samples; and (g) storingall hyperspectral sample hypercubes to form the database.

D2. A computer database stored in a computer readable medium, producedin accordance with paragraph D2.

E1. A method for classifying a candidate agricultural product varietyfor use as a substitute for a blend component of a blended agriculturalproduct, the method comprising the steps of: (a) establishing a spectralfingerprint for the agricultural product blend component to besubstituted; (b) establishing a spectral fingerprint for the candidateagricultural product variety; (c) comparing the spectral fingerprint forthe candidate agricultural product variety to the spectral fingerprintfor the agricultural product blend component; and (d) if the spectralfingerprint for the candidate agricultural product variety issubstantially similar to the spectral fingerprint for the agriculturalproduct blend component, substitute the candidate agricultural productvariety for at least a portion of the blend component in the blendedagricultural product.

E2. The method of paragraph E1, wherein the agricultural product blendcomponent is tobacco.

It is to be fully understood that certain aspects, characteristics, andfeatures, of the forms disclosed herein, which are illustrativelydescribed and presented in the context or format of a plurality ofseparate forms, may also be illustratively described and presented inany suitable combination or sub-combination in the context or format ofa single form. Conversely, various aspects, characteristics, andfeatures, of the forms disclosed herein, which are illustrativelydescribed and presented in combination or sub-combination in the contextor format of a single form, may also be illustratively described andpresented in the context or format of a plurality of separate forms.

All patents, patent applications, and publications, cited or referred toin this specification are herein incorporated in their entirety byreference into the specification, to the same extent as if eachindividual patent, patent application, or publication, was specificallyand individually indicated to be incorporated herein by reference. Inaddition, citation or identification of any reference in thisspecification shall not be construed or understood as an admission thatsuch reference represents or corresponds to prior art. To the extentthat section headings are used, they should not be construed asnecessarily limiting.

While the forms disclosed herein have been described in connection witha number of exemplary forms, and implementations, the forms disclosedherein are not so limited, but rather cover various modifications, andequivalent arrangements, which fall within the purview of the presentclaims.

What is claimed:
 1. A method for replacing a blend component of ablended agricultural product with a substitute agricultural product, themethod comprising: establishing a spectral fingerprint for anagricultural product blend component to be substituted; establishing aspectral fingerprint for a candidate agricultural product; identifyingthe candidate agricultural product as a substitute agricultural productbased on a determination that the spectral fingerprint for the candidateagricultural product is similar to the spectral fingerprint for theagricultural product blend component; and replacing at least a portionof the agricultural product blend component with the substituteagricultural product in a blended agricultural product.
 2. The method ofclaim 1, wherein the agricultural product blend component is tobacco. 3.The method of claim 1, further comprising: correlating one or morefeatures of the spectral fingerprints that correspond to sensoryattributes.
 4. The method of claim 1, further comprising: correlatingone or more features of the spectral fingerprints that correspond tophysical characteristics, chemical characteristics, or both physical andchemical characteristics.
 5. The method of claim 1, further comprising:blending the blended agricultural product by a computer controlledsystem.
 6. The method of claim 1, further comprising: establishing aspectral fingerprint for a second candidate agricultural product,identifying the second candidate agricultural product as a secondsubstitute agricultural product based on a determination that thespectral fingerprint for the second candidate agricultural product issimilar to the spectral fingerprint for the agricultural product blendcomponent, and replacing at least a second portion of the agriculturalproduct blend component with the second substitute agricultural productin the blended agricultural product.
 7. The method of claim 1, whereinthe determining that the spectral fingerprint for the candidateagricultural product is similar to the spectral fingerprint for theagricultural product blend component comprises: dividing each of thespectral fingerprint for the candidate agricultural product and thespectral fingerprint for the agricultural product blend component intoevenly-spaced grids; determining a spectral profile by compiling a listof characteristic spectra that are similar to spectra in each spectralfingerprint; and determining that the spectral fingerprint for thecandidate agricultural product is similar to the spectral fingerprintfor the agricultural product blend component based on whether more thana threshold number of characteristic spectra are the same between thespectral fingerprint for the candidate agricultural product and thespectral fingerprint for the agricultural product blend component. 8.The method of claim 1, further comprising: determining a candidate imagecube for the candidate agricultural product and a product image cube forthe agricultural product blend component, wherein each image cubecontains more than one thousand spectra.
 9. The method of claim 8,further comprising: summing adjacent wavelengths for each spectra of thecandidate image cube to increase a signal to noise ratio, reduce filesize, and decrease processing time.
 10. The method of claim 8, furthercomprising: performing spectra extraction and spectra matching on thecandidate image cube and the product image cube to determine that thespectral fingerprint for the candidate agricultural product is similarto the spectral fingerprint for the agricultural product blendcomponent.
 11. The method of claim 1, wherein the agricultural productblend component is a blend of a plurality of products.
 12. The method ofclaim 1, wherein the agricultural product blend component is a tobaccoblend.